AI to monitor changes in social behaviour for the early detection of disease in dairy cattle

在英国，牛奶是经济的重要组成部分，也是重要的营养来源。有几种疾病经常在英国奶牛中发展，危害健康和福利，并导致农民和工业的经济损失。人们还发现，生病的奶牛对甲烷排放的贡献不成比例，因此影响了该行业的环境可持续性。此外，高福利比以往任何时候都更重要，以满足社会对粮食生产的需求。为了帮助农民检测和治疗这些疾病，现在有许多奶牛自动监测解决方案可供农民使用。所有这些技术的一个关键缺点是，它们侧重于检测可观察到的晚期疾病症状，此时治疗选择可能有限，产奶量持续减少，动物福利受到更严重损害。奶牛对感染和创伤的反应是不优先考虑对生存和恢复不是立即必要的行为——比如社会互动——而优先考虑那些长期重要的行为。在最近的一项研究中，我们发现，早期乳腺炎患者的社会探索、为他人梳理毛发和接受头撞的次数较低。因此，我们假设社会行为的改变可能是疾病的早期预测因素。对于忙碌的农民来说，检测社会行为的变化是很困难的，但通过在关键的焦点上监测它们是可能的，比如在排队挤奶或在饲料铺喂奶时，使用摄像机和人工智能（AI）。我们已经开发出高度强大的人工智能，可以在视频中跟踪奶牛的运动，并通过它们独特的皮毛图案识别每一头奶牛。其他人现在已经从视频中展示了良好的从属和敌对社会互动分类，因此我们现在建议将这两种想法结合起来，以跟踪牛群中每只已识别的奶牛的活动和社会行为随时间的变化。从我们的约翰奥德克中心奶牛场的主谷仓的64台摄像机收集了两年的视频，我们将训练一个模型，该模型将学习随着时间的推移，哪些类型的行为会发生变化，这些行为表明了不同的早期疾病。我们将重点关注乳腺炎和跛行，因为这些疾病在我们的数据中发病率最高，也是英国乳制品行业最重要的疾病。与此同时，我们将对我们的一小群人的唾液进行采样，这样我们就可以确定炎症的一般水平，使我们能够看到我们的行为预测因子对特定疾病的特异性。奶农是他们的牛的行为和个性方面的专家，他们的投入对于帮助理解农场数据的变幻莫测以及我们的系统如何运作至关重要。我们将通过在招募的农场网络中部署该系统来测试我们的系统，并将对农民进行深入的半结构化访谈，了解他们对摄像机放置的体验（包括农场工人的侵入性和社会接受度），操作以及对农场，农场工人或动物管理，健康和福利的任何其他感知影响。同样重要的是，我们在设计系统时要考虑到行业的各个方面，在设置警报级别、设计便于采用的用户友好界面和讨论其他市场途径（如疾病监测）方面，利用他们的不同见解和专业知识。因此，我们组建了一个合作伙伴联盟，涵盖了从农民到兽医、供应链、数据/诊断服务提供商和业务发展等所有关键领域，我们都有成功参与和影响的良好记录。透过谘询，我们会制定一套可持续发展的策略，让非业界持份者和公众有意义地参与我们的制度和成果，从而促进公众/持份者对制度的广泛了解和接受。

In the UK, dairy milk is a key part of the economy and an important source of nutrition. There are several diseases that regularly develop in UK dairy cows which compromise health and welfare, and lead to economic losses for the farmer and industry. Ill cows have also been found to contribute disproportionately to methane emissions and hence the environmental sustainability of the sector. In addition, high welfare is more important than ever to satisfy societal demands for food production.To help farmers detect and treat these diseases, numerous solutions for automated monitoring of dairy cattle are now available to farmers. A critical disadvantage of all these technologies is that they are focussed on detecting the observable symptoms of later stage disease, when treatment options may be limited, reduction of milk production persistent and animal welfare more severely compromised.A cow's response to infection and trauma is to de-prioritise behaviours not immediately essential to survival and recovery - such as social interactions - in favour of those that remain critical for longer, In a recent study we have found that social exploration, the grooming of others and receiving headbutts were lower in individuals with early stage mastitis. We hence hypothesise that social behaviour changes could be early predictors of disease.Detecting social behaviour changes is difficult for the busy farmer, but is possible by monitoring them at key focal points, such as when queueing for milking or feeding at the feed bunk, using video cameras and artificial intelligence (AI). We have developed highly robust AI that can track the motion of cows in video and recognises each individual through their distinctive coat pattern. Others have now demonstrated good classification of affiliative and agonistic social interactions from video and hence we now propose combining the two ideas to track changes in activities and social behaviours over time for each identified cow in a herd. From collecting two years of video from 64 cameras covering the main barn at our John Oldacre Centre dairy farm, we will train a model that learns what types of behaviours change over time that are indicative of different early stage diseases. We will focus on mastitis and lameness, as these diseases have the greatest incidence in our data and are the most important for the UK dairy industry. At the same time, we will sample the saliva of a subset of our herd so we can determine general levels of inflammation, enabling us to see how specific our behavioural predictors are to particular diseases.Dairy farmers are specialists in the behaviour and personalities of their cattle and their input will be vital to helping understand vagaries in farm data and how our system is functioning. We will test our system by deploying it at a network of recruited farms, and will conduct in-depth semi-structured interviews with the farmers regarding their experiences of camera placement (including intrusiveness and social acceptance by farm workers), operation and any other perceived impacts to their farms, farm workers or animal management, health and welfare. It is also critical that we design the system with all facets of industry, to engage their diverse insights and expertise in setting alert levels, designing user-friendly interfaces that will be well placed to be uptaken and discussing additional routes to market such as for disease surveillance. We have therefore assembled a consortium of partners covering all key areas from farmers to vets, the supply chain, data/diagnostic service providers and business development, all of whom we have a proven track record of successful engagement and impact with. Through consultation we will develop a sustainable strategy for meaningful lay stakeholder and public involvement with our system and results, helping to promote a widespread understanding and public/stakeholder acceptance of the system.